Zhao Jing, Ma Ruoming, Sun Jian, Zhang Rongji, Zhang Cheng
Department of Traffic Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
College of Transportation Engineering, Tongji University, Shanghai 201804, China.
Fundam Res. 2023 Nov 5;5(4):1645-1658. doi: 10.1016/j.fmre.2023.08.008. eCollection 2025 Jul.
The dispersion of vehicular paths is a common phenomenon in the inner area of signalized intersections due to heterogeneous driver behavior and interactions. This study aims to develop an explainable neural network-based model to describe the vehicle path dispersion by exploring the relationship between the path dispersion and external factors. A backpropagation neural network model was established to analyze the effects of external factors on the dispersion of through and left-turn paths based on real trajectory data collected from 20 intersections in Shanghai, China. Twelve influencing factors in varying geometric, traffic, signalization, and traffic management conditions were considered. The predictive power and transferability of the model were verified by applying the trained model on the four new intersections. The contributions of the influencing factors on the path dispersion were explored based on the neural interpretation diagram, relative importance of influencing factors, and sensitivity analysis to offer explanatory insights for the proposed model. The results show that the mean absolute percentage errors of the path dispersion models for the through and left-turn movements are only 14.67% and 17.65%, respectively. The through path dispersion is primarily influenced by the number of exit lanes, the offset degree between the approach and exit lanes, and the traffic saturation degree on the through lane. In contrast, the path dispersion of the left turn is mainly affected by the number of exit lanes, the left-turn angle, and the setting of guide lines.
由于驾驶员行为和交互的异质性，车辆路径离散是信号交叉口内部区域的常见现象。本研究旨在通过探索路径离散与外部因素之间的关系，开发一种基于可解释神经网络的模型来描述车辆路径离散。基于从中国上海20个交叉口收集的真实轨迹数据，建立了反向传播神经网络模型，以分析外部因素对直行车道和左转车道离散的影响。考虑了不同几何、交通、信号控制和交通管理条件下的12个影响因素。通过将训练好的模型应用于4个新的交叉口，验证了模型的预测能力和可转移性。基于神经解释图、影响因素的相对重要性和敏感性分析，探索了影响因素对路径离散的贡献，为所提出的模型提供解释性见解。结果表明，直行车道和左转车道路径离散模型的平均绝对百分比误差分别仅为14.67%和17.65%。直行车道的路径离散主要受出口车道数量、进口车道与出口车道的偏移程度以及直行车道上的交通饱和度影响。相比之下，左转车道的路径离散主要受出口车道数量、左转角度和导向线设置的影响。
